Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1Model

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

A trained machine learning Model.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1Model

Returns a new instance of GoogleCloudAiplatformV1Model.



17188
17189
17190
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17188

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#artifact_uri ⇒ String

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models. Corresponds to the JSON property artifactUri

Returns:

  • (String) 


16943
16944
16945
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16943

def artifact_uri
  @artifact_uri
end

#base_model_source ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelBaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. Corresponds to the JSON property baseModelSource

Returns:



16949
16950
16951
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16949

def base_model_source
  @base_model_source
end

#checkpoints ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1Checkpoint>

Optional. Output only. The checkpoints of the model. Corresponds to the JSON property checkpoints

Returns:



16954
16955
16956
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16954

def checkpoints
  @checkpoints
end

#container_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification. Corresponds to the JSON property containerSpec

Returns:



16962
16963
16964
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16962

def container_spec
  @container_spec
end

#create_time ⇒ String

Output only. Timestamp when this Model was uploaded into Vertex AI. Corresponds to the JSON property createTime

Returns:

  • (String) 


16967
16968
16969
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16967

def create_time
  @create_time
end

#data_stats ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDataStats

Stats of data used for train or evaluate the Model. Corresponds to the JSON property dataStats

Returns:



16972
16973
16974
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16972

def data_stats
  @data_stats
end

#default_checkpoint_id ⇒ String

The default checkpoint id of a model version. Corresponds to the JSON property defaultCheckpointId

Returns:

  • (String) 


16977
16978
16979
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16977

def default_checkpoint_id
  @default_checkpoint_id
end

#deployed_models ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1DeployedModelRef>

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations. Corresponds to the JSON property deployedModels

Returns:



16983
16984
16985
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16983

def deployed_models
  @deployed_models
end

#description ⇒ String

The description of the Model. Corresponds to the JSON property description

Returns:

  • (String) 


16988
16989
16990
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16988

def description
  @description
end

#display_name ⇒ String

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters. Corresponds to the JSON property displayName

Returns:

  • (String) 


16994
16995
16996
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 16994

def display_name
  @display_name
end

#encryption_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top- level resource. Corresponds to the JSON property encryptionSpec

Returns:



17000
17001
17002
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17000

def encryption_spec
  @encryption_spec
end

#etag ⇒ String

Used to perform consistent read-modify-write updates. If not set, a blind " overwrite" update happens. Corresponds to the JSON property etag

Returns:

  • (String) 


17006
17007
17008
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17006

def etag
  @etag
end

#explanation_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ExplanationSpec

Specification of Model explanation. Corresponds to the JSON property explanationSpec

Returns:



17011
17012
17013
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17011

def explanation_spec
  @explanation_spec
end

#labels ⇒ Hash<String,String>

The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Corresponds to the JSON property labels

Returns:

  • (Hash<String,String>) 


17020
17021
17022
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17020

def labels
  @labels
end

#metadata ⇒ Object

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information. Corresponds to the JSON property metadata

Returns:

  • (Object) 


17027
17028
17029
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17027

def metadata
  @metadata
end

#metadata_artifact ⇒ String

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/project/locations/location/metadataStores/metadata_store/ artifacts/artifact`. Corresponds to the JSON propertymetadataArtifact`

Returns:

  • (String) 


17035
17036
17037
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17035

def metadata_artifact
  @metadata_artifact
end

#metadata_schema_uri ⇒ String

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. Corresponds to the JSON property metadataSchemaUri

Returns:

  • (String) 


17048
17049
17050
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17048

def metadata_schema_uri
  @metadata_schema_uri
end

#model_source_info ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelSourceInfo

Detail description of the source information of the model. Corresponds to the JSON property modelSourceInfo

Returns:



17053
17054
17055
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17053

def model_source_info
  @model_source_info
end

#name ⇒ String

The resource name of the Model. Corresponds to the JSON property name

Returns:

  • (String) 


17058
17059
17060
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17058

def name
  @name
end

#original_model_info ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelOriginalModelInfo

Contains information about the original Model if this Model is a copy. Corresponds to the JSON property originalModelInfo

Returns:



17063
17064
17065
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17063

def original_model_info
  @original_model_info
end

#pipeline_job ⇒ String

Optional. This field is populated if the model is produced by a pipeline job. Corresponds to the JSON property pipelineJob

Returns:

  • (String) 


17068
17069
17070
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17068

def pipeline_job
  @pipeline_job
end

#predict_schemata ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob. Corresponds to the JSON property predictSchemata

Returns:



17074
17075
17076
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17074

def predict_schemata
  @predict_schemata
end

#satisfies_pzi ⇒ Boolean Also known as: satisfies_pzi?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzi

Returns:

  • (Boolean) 


17079
17080
17081
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17079

def satisfies_pzi
  @satisfies_pzi
end

#satisfies_pzs ⇒ Boolean Also known as: satisfies_pzs?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzs

Returns:

  • (Boolean) 


17085
17086
17087
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17085

def satisfies_pzs
  @satisfies_pzs
end

#supported_deployment_resources_types ⇒ Array<String>

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService. Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats. Corresponds to the JSON property supportedDeploymentResourcesTypes

Returns:

  • (Array<String>) 


17099
17100
17101
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17099

def supported_deployment_resources_types
  @supported_deployment_resources_types
end

#supported_export_formats ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelExportFormat>

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export. Corresponds to the JSON property supportedExportFormats

Returns:



17105
17106
17107
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17105

def supported_export_formats
  @supported_export_formats
end

#supported_input_storage_formats ⇒ Array<String>

Output only. The formats this Model supports in BatchPredictionJob. input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. Corresponds to the JSON property supportedInputStorageFormats

Returns:

  • (Array<String>) 


17124
17125
17126
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17124

def supported_input_storage_formats
  @supported_input_storage_formats
end

#supported_output_storage_formats ⇒ Array<String>

Output only. The formats this Model supports in BatchPredictionJob. output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata. prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. Corresponds to the JSON property supportedOutputStorageFormats

Returns:

  • (Array<String>) 


17142
17143
17144
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17142

def supported_output_storage_formats
  @supported_output_storage_formats
end

#training_pipeline ⇒ String

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any. Corresponds to the JSON property trainingPipeline

Returns:

  • (String) 


17148
17149
17150
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17148

def training_pipeline
  @training_pipeline
end

#update_time ⇒ String

Output only. Timestamp when this Model was most recently updated. Corresponds to the JSON property updateTime

Returns:

  • (String) 


17153
17154
17155
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17153

def update_time
  @update_time
end

#version_aliases ⇒ Array<String>

User provided version aliases so that a model version can be referenced via alias (i.e. projects/project/locations/location/models/model_id@ version_alias`instead of auto-generated version id (i.e.projects/project/ locations/location/models/model_id@version_id). The format is a-z0,126 [a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model. Corresponds to the JSON propertyversionAliases`

Returns:

  • (Array<String>) 


17164
17165
17166
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17164

def version_aliases
  @version_aliases
end

#version_create_time ⇒ String

Output only. Timestamp when this version was created. Corresponds to the JSON property versionCreateTime

Returns:

  • (String) 


17169
17170
17171
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17169

def version_create_time
  @version_create_time
end

#version_description ⇒ String

The description of this version. Corresponds to the JSON property versionDescription

Returns:

  • (String) 


17174
17175
17176
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17174

def version_description
  @version_description
end

#version_id ⇒ String

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation. Corresponds to the JSON property versionId

Returns:

  • (String) 


17181
17182
17183
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17181

def version_id
  @version_id
end

#version_update_time ⇒ String

Output only. Timestamp when this version was most recently updated. Corresponds to the JSON property versionUpdateTime

Returns:

  • (String) 


17186
17187
17188
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17186

def version_update_time
  @version_update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



17193
17194
17195
17196
17197
17198
17199
17200
17201
17202
17203
17204
17205
17206
17207
17208
17209
17210
17211
17212
17213
17214
17215
17216
17217
17218
17219
17220
17221
17222
17223
17224
17225
17226
17227
17228
17229
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17193

def update!(**args)
  @artifact_uri = args[:artifact_uri] if args.key?(:artifact_uri)
  @base_model_source = args[:base_model_source] if args.key?(:base_model_source)
  @checkpoints = args[:checkpoints] if args.key?(:checkpoints)
  @container_spec = args[:container_spec] if args.key?(:container_spec)
  @create_time = args[:create_time] if args.key?(:create_time)
  @data_stats = args[:data_stats] if args.key?(:data_stats)
  @default_checkpoint_id = args[:default_checkpoint_id] if args.key?(:default_checkpoint_id)
  @deployed_models = args[:deployed_models] if args.key?(:deployed_models)
  @description = args[:description] if args.key?(:description)
  @display_name = args[:display_name] if args.key?(:display_name)
  @encryption_spec = args[:encryption_spec] if args.key?(:encryption_spec)
  @etag = args[:etag] if args.key?(:etag)
  @explanation_spec = args[:explanation_spec] if args.key?(:explanation_spec)
  @labels = args[:labels] if args.key?(:labels)
  @metadata = args[:metadata] if args.key?(:metadata)
  @metadata_artifact = args[:metadata_artifact] if args.key?(:metadata_artifact)
  @metadata_schema_uri = args[:metadata_schema_uri] if args.key?(:metadata_schema_uri)
  @model_source_info = args[:model_source_info] if args.key?(:model_source_info)
  @name = args[:name] if args.key?(:name)
  @original_model_info = args[:original_model_info] if args.key?(:original_model_info)
  @pipeline_job = args[:pipeline_job] if args.key?(:pipeline_job)
  @predict_schemata = args[:predict_schemata] if args.key?(:predict_schemata)
  @satisfies_pzi = args[:satisfies_pzi] if args.key?(:satisfies_pzi)
  @satisfies_pzs = args[:satisfies_pzs] if args.key?(:satisfies_pzs)
  @supported_deployment_resources_types = args[:supported_deployment_resources_types] if args.key?(:supported_deployment_resources_types)
  @supported_export_formats = args[:supported_export_formats] if args.key?(:supported_export_formats)
  @supported_input_storage_formats = args[:supported_input_storage_formats] if args.key?(:supported_input_storage_formats)
  @supported_output_storage_formats = args[:supported_output_storage_formats] if args.key?(:supported_output_storage_formats)
  @training_pipeline = args[:training_pipeline] if args.key?(:training_pipeline)
  @update_time = args[:update_time] if args.key?(:update_time)
  @version_aliases = args[:version_aliases] if args.key?(:version_aliases)
  @version_create_time = args[:version_create_time] if args.key?(:version_create_time)
  @version_description = args[:version_description] if args.key?(:version_description)
  @version_id = args[:version_id] if args.key?(:version_id)
  @version_update_time = args[:version_update_time] if args.key?(:version_update_time)
end